The prevalence of obesity in developed countries is increasing at an alarming rate. Obesity contributes to an increased risk of heart disease, hypertension, diabetes, and some cancers and is now considered a risk factor for cardiovascular disease. The objective of this research is to investigate application of novel noninvasive devices and pattern recognition methods to perform studies of human food intake behavior and produce objective estimates of volumetric and caloric food intake that will be relevant for identifying effective measures to treat or prevent diseases like obesity. Such devices and methods could extend our understanding of causes of obesity, and the monitoring devices created in this study could be used for monitoring of obese patients and as a part of a therapy potentially improve quality of life and decrease the morbidity and mortality associated with obesity. The goal of this study is to design and perform a pilot investigation which will provide preliminary data that objective observations of mastication (chewing) and deglutition (swallowing) by a wearable, non-intrusive monitoring device can provide statistically reliable estimates of eating habits by providing objective measures of delution frequency, duration of mastication and identifying periods of food intake with sufficient sensitivity and specificity. The aims of the proposed research include design of the wearable sensors and associated signal processing methods; development of pattern recognition methodologies that will automatically detect instances of delution and mastication from sensor recordings; development of pattern recognition methods to automatically identify periods of food intake based on detected chewing and swallowing; and to validate these device and methodologies on a group of human subjects. Modern methods of computational intelligence such as artificial neural networks and fuzzy logic will be used along with statistical methods to achieve the highest 'accuracy of pattern recognition.